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Applied Predictive Modeling

Kuhn, Max, Johnson, Kjell·2013·Springer eBooks

DOI 10.1007/978-1-4614-6849-3

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“The performance of a model on a test dataset is an indicator of how well a model might perform in the future on previously not seen data (Kuhn and Johnson 2013).”
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“In particular, the AUC provides a good performance measure when the distribution of the predicted categories is imbalanced (Kuhn and Johnson 2013).”
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“Whereas parameters are elements of the model that are directly learnt from the training data, hyperparameters cannot180 be directly estimated from the training data as there is no analytical formula available to calculate an appropriate value (Kuhn and Johnson, 2013).”
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“Here, the bootstrap sample performance variability gives an estimate of the variability of performance using only a single test set (Hastie et al., 2009; Kuhn & Johnson, 2013).”
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“…prediction, is a useful way to tune the parsimony of a model because predictive exercises allow one to judge how much impact is being made by each element of the model in terms of its contribution to predictive performance (Hastie, Tibshirani, and Friedman 2009; Kuhn and Johnson 2013).”
Background
Multi-Dimensional Infrastructure Resilience Modeling: An Application to Hurricane-Prone Electric Power Distribution Systems
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“Feature selection, particularly feature engineering, can contribute positively to the accuracy of prediction models due to the information gain obtained from the association of multiple input variables (Abbott, 2014; Kuhn & Johnson, 2013).”
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“The objective is to remove the features that may be non-informative or redundant predictors in the model [56].”
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“We adopted the cross-validation setting used in many different works [46]–[53].”
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“referred to as tuning parameters because there is no analytical formula available to calculate an appropriate value for them (Kuhn and Johnson, 2013).”
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“In an effort to minimize overfitting and identify a model that would best generalize to a holdout sample, we ran each of the 14 algorithms within 10-folds cross-validation, such that the training dataset (n = 387) was split into ten equal size samples (Kuhn & Johnson, 2013).”
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“Model tuning was automated via repeated k-fold cross-validation [Hastie et al., 2009; James et al., 2013; Kuhn and Johnson, 2013] with 10 repeats and 10 folds to optimize model performance via the minimization of RMSE, with implementation conducted in R using the caret package [Kuhn, 2008; Kuhn and…”
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“, SVM, RF and NN), this complete RFE scheme described must be accomplished on each one of these proposed models [37], which becomes computationally burdensome.”
Method
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“Although many physicians would concur with Kuhn and Johnson [14] that “as long as the model can be appropriately validated, it should not matter whether it is a black box or a simple, interpretable model”, others could make a strong argument that this would be unethical in medicine.”
Background
A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset
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“In order to ensure an approximately equal distribution of the response variable in both data sets, stratified random sampling is applied (Kuhn and Johnson 2013).”
Method
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“There are three main parameters (Kuhn and Johnson, 2013): M is the total number of boosting iterations (n.trees), v is the learning rate (shrinkage coefficient) and J is the number of terminal nodes (tree size).”
BackgroundMethodInfluential
Modern Prediction Methods
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“Both Hastie et al. (2009) and Kuhn and Johnson (2013) provide more specific guidance to researchers on which prediction methods are best suited to a given situation based on characteristics of one’s modeling situation.”
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“Accordingly, most filter methods evaluate each predictor individually and ignore possibly important predictor interactions, whereas wrapper methods result in an increase in computation time [23].”
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“An over-fitted model performs very well on the data on which the model was built, but usually has poor accuracy on a new data sample (Kuhn and Johnson 2013).”
BackgroundMethodResultInfluential
From statistical‐ to machine learning‐based network traffic prediction
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“…into two categories: model parameters, which may be initiated and adjusted via data learning; and hyperparameters, which cannot be easily approximated from data learning and should be established prior to training an ML model since they determine the model architecture [32], [33], [34], [35].”
Background
Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential
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“…need to test for generalisability when, for example, using a CART to estimate variable importance, as compared to the need to a predictive classi ﬁ cation model, because an estimation of variable importance does not explicitly generalise beyond the learnt parameter space (Kuhn and Johnson, 2013).”
Method
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“A compact, parsimonious and non-redundant subset of features should contribute to speed up the learning process and improve the generalization of predictive models (Tuv et al., 2009; Kuhn and Johnson, 2013; Louppe, 2014).”
Background
Understanding the effects of different review features on purchase probability
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“To make sure that we are not overfitting, we use five-fold cross validation by assigning a random value 1–5 to each case (Kuhn and Johnson 2013, 69–70).”
Method
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García, F. C., Alonso‐Sarría, F., Gomariz‐Castillo, F., & Oñate-Valdivieso, F. (2017) · Computers & Geosciences · doi:10.1016/j.cageo.2017.02.012 · Cited by 91
Machine Learning for Predicting Patient Wait Times and Appointment Delays
Curtis, C., Liu, C., Bollerman, T. J., & Pianykh, O. S. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.08.021 · Cited by 90
“Since then, driven by scientific research and growth in computing power, ML evolved rapidly, enriching itself with a wealth of diverse computational approaches [7,8,10,11].”
Background
Group communication analysis: A computational linguistics approach for detecting sociocognitive roles in multiparty interactions
Nixon, N., Nixon, T. M., & Graesser, A. C. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1102-z · Cited by 134
Preventing rather than punishing: An early warning model of malfeasance in public procurement
Gallego, J., Rivero, G., & Martínez, J. D. S. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.06.006 · Cited by 123
“Such imbalance sometimes, but not always, causes issues with classification tasks (Kuhn & Johnson, 2013) using metrics like accuracy, because models can attain high performance by predicting all instances as belonging to the majority class.”
Background
Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media
Kühl, N., Mühlthaler, M., & Goutier, M. (2019) · Electronic Markets · doi:10.1007/s12525-019-00351-0 · Cited by 103
On the utilization of deep and ensemble learning to detect milk adulteration
Neto, H. A., Tavares, W. L. F., Ribeiro, D. C., Alves, R. C. D. O., Fonseca, L. M., & Campos, S. (2019) · BioData Mining · doi:10.1186/s13040-019-0200-5 · Cited by 93
On the use of machine learning algorithms in forensic anthropology
Nikita, E., & Nikitas, P. (2020) · Legal Medicine · doi:10.1016/j.legalmed.2020.101771 · Cited by 100
Drivers of Fire Anomalies in the Brazilian Amazon: Lessons Learned from the 2019 Fire Crisis
Silveira, M. V. F., Petri, C. A., Broggio, I. S., Chagas, G. O., Macul, M. D. S., Leite, C., Ferrari, E. M. M., Amim, C. G. V., de Freitas, A. L. R., Motta, A. Z., et al. (2020) · Land · doi:10.3390/land9120516 · Cited by 97
“The other two landscape metrics presented high AUC values only in AM and AP.”
MethodInfluential
Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss
Chamseddine, E., Mansouri, N., Soui, M., & Abed, M. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109588 · Cited by 112
“In predictive modeling, imbalanced classifications are a challenging problem because most ML classification algorithms were developed with the assumption that all the classes have the same number of samples [76].”
Background
Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type
Qin, Y., Wu, J., Wen, X., Wang, K., Huang, A., Liu, B., Yu, J., Li, C., Yu, F., & Ren, Z. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192215027 · Cited by 108
“That is, the number of samples in one category is much larger than in the other [26,27].”
Background
Assessing and improving the transferability of current global spatial prediction models
Ludwig, M., Moreno‐Martinez, A., Hölzel, N., Pebesma, E., & Meyer, H. (2023) · Global Ecology and Biogeography · doi:10.1111/geb.13635 · Cited by 92
Applying Knowledge-Guided Machine Learning to Slope Stability Prediction
Pei, T., Qiu, T., & Shen, C. (2023) · Journal of Geotechnical and Geoenvironmental Engineering · doi:10.1061/jggefk.gteng-11053 · Cited by 97
The importance of prediction model validation and assessment in obesity and nutrition research
Ivanescu, A. E., Li, P., George, B. J., Brown, A. W., Keith, S. W., Raju, D. A., & Allison, D. B. (2015) · International Journal of Obesity · doi:10.1038/ijo.2015.214 · Cited by 97
Combined effects of nitrogen to phosphorus and nitrate to ammonia ratios on cyanobacterial metabolite concentrations in eutrophic Midwestern USA reservoirs
Harris, T. D., Smith, V. H., Graham, J. L., et al. (2016) · Inland Waters · doi:10.5268/iw-6.2.938 · Cited by 90
“Multivariate relations between water quality parameters (TN, TP, TN:TP, NO3, NH3, NO3:NH3), cyanobacteria, and cyanobacterial secondary metabolites were determined using principal components analysis (PCA) as described by Kuhn and Johnson (2013).”
Method
Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets
Ren, K., Fang, W., Qu, J., Zhang, X., & Shi, X. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124897 · Cited by 89
Improving healthcare operations management with machine learning
Pianykh, O. S., Guitron, S., Parke, D., Zhang, C., Pandharipande, P., Brink, J., & Rosenthal, D. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0176-3 · Cited by 87
Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)
Selvaraj, M. G., Valderrama, M., Guzman, D., Valencia-Ortiz, M., Ruiz, H., & Acharjee, A. (2020) · Plant Methods · doi:10.1186/s13007-020-00625-1 · Cited by 92
Machine learning models for streamflow regionalization in a tropical watershed
Ferreira, R. G., da Silva, D. D., Elesbon, A. A. A., et al. (2020) · Journal of Environmental Management · doi:10.1016/j.jenvman.2020.111713 · Cited by 89
Systematic analysis of the impact of slurry coating on manufacture of Li-ion battery electrodes via explainable machine learning
Niri, M. F., Reynolds, C. D., Román‐Ramírez, L. A., Kendrick, E., & Marco, J. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.06.036 · Cited by 93
“For this purpose, the cross validation (CV) approach is utilised [60].”
Method
Use of WorldView-2 stereo imagery and National Forest Inventory data for wall-to-wall mapping of growing stock
Immitzer, M., Stepper, C., Böck, S., Straub, C., & Atzberger, C. (2015) · Forest Ecology and Management · doi:10.1016/j.foreco.2015.10.018 · Cited by 90
Machine Learning Based Predictive Modeling of Debris Flow Probability Following Wildfire in the Intermountain Western United States
Kern, A. N., Addison, P. E., Oommen, T., et al. (2017) · Mathematical Geosciences · doi:10.1007/s11004-017-9681-2 · Cited by 88
“Instead of defining the distribution for each predictor individually, the distributions are combined by class (Kuhn and Johnson 2013).”
BackgroundMethodInfluential
Why input matters: Selection of climate data sets for modelling the potential distribution of a treeline species in the Himalayan region
Bobrowski, M., & Schickhoff, U. (2017) · Ecological Modelling · doi:10.1016/j.ecolmodel.2017.05.021 · Cited by 93
Enhanced estimations of post‐stroke aphasia severity using stacked multimodal predictions
Pustina, D., Coslett, H. B., Ungar, L., Faseyitan, O. K., Medaglia, J. D., Avants, B., & Schwartz, M. F. (2017) · Human Brain Mapping · doi:10.1002/hbm.23752 · Cited by 86
“RF can (1) detect linear and nonlinear relationships, (2) detect variable interactions, and (3) utilize a large number of variables with minimal overfitting [Kuhn and Johnson, 2013].”
BackgroundMethodInfluential

Authors: Kuhn, M., & Johnson, K.
Venue: Springer eBooks
Publisher: Springer New York
Published: 2013
DOI: 10.1007/978-1-4614-6849-3
OpenAlex: W429766147
Cited by: 6,788
References: 0
Citations by year: 2026: 564 · 2025: 813 · 2024: 727 · 2023: 776 · 2022: 810 · 2021: 815 · 2020: 715 · 2019: 492 · 2018: 382 · 2017: 271 · 2016: 198 · 2015: 156 · 2014: 61 · 2013: 5
FWCI: 32.11
Type: book
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Springer eBooks (publishedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Keywords: predictive modeling, data preprocessing, data partitioning, model calibration, model selection, model evaluation, classification, regression analysis

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